The best clue might come from a 2022 paper written by the Anthropic team back when their startup was just a year old. They warned that the incentives in the AI industry — think profit and prestige — will push companies to “deploy large generative models despite high uncertainty about the full extent of what these models are capable of.” They argued that, if we want safe AI, the industry’s underlying incentive structure needs to change.
Well, at three years old, Anthropic is now the age of a toddler, and it’s experiencing many of the same growing pains that afflicted its older sibling OpenAI. In some ways, they’re the same tensions that have plagued all Silicon Valley tech startups that start out with a “don’t be evil” philosophy. Now, though, the tensions are turbocharged.
An AI company may want to build safe systems, but in such a hype-filled industry, it faces enormous pressure to be first out of the gate. The company needs to pull in investors to supply the gargantuan sums of money needed to build top AI models, and to do that, it needs to satisfy them by showing a path to huge profits. Oh, and the stakes — should the tech go wrong — are much higher than with almost any previous technology.
So a company like Anthropic has to wrestle with deep internal contradictions, and ultimately faces an existential question: Is it even possible to run an AI company that advances the state of the art while also truly prioritizing ethics and safety?
“I don’t think it’s possible,” futurist Amy Webb, the CEO of the Future Today Institute, told me a few months ago.
They are deterministic though, in a literal sense. Rather their behavior is undefined. And yes, a LLM is not a person and it’s not quite accurate to talk about them knowing or understanding things. So what though? Why would that be any sort of evidence that research efforts into AI safety are futile? This is at least as much of an engineering problem as a philosophy problem.
The output for a given input cannot be independently calculated as far as I know, particularly when random seeds are part of the input. How is that deterministic?
The so what means trying to prevent certain outputs based on moral judgements isn’t possible. It wouldn’t really be possible if you could get in there with code and change things unless you could write code for morality, but it’s doubly impossible given you can’t.
The system gives a probability distribution for the next word based on the prompt, which will always be the same for a given input. That meets the definition of deterministic. You might choose to add non-deterministic rng to the input or output, but that would be a choice and not something inherent to how LLMs work. Random ‘seeds’ are normally used as part of deterministically repeatable rng. I’m not sure what you mean by “independently” calculated, you can calculate the output if you have the model weights, you likely can’t if you don’t, but that doesn’t affect how deterministic it is.
The impossibility of defining morality in precise terms, or even coming to an agreement on what correct moral judgment even is, obviously doesn’t preclude all potentially useful efforts to apply it. For instance since there is a general consensus that people being electrocuted is bad, electrical cables normally are made with their conductive parts encased in non-conductive material, a practice that is successful in reducing how often people get electrocuted. Why would that sort of thing be uniquely impossible for LLMs? Just because they are logic processing systems that are more grown than engineered? Because they are sort of anthropomorphic but aren’t really people? The reasoning doesn’t follow. What people are complaining about here is that AI companies are not making these efforts a priority, and it’s a valid complaint because it isn’t the case that these systems are going to be the same amount of dangerous no matter how they are made or used.